Results 211 to 220 of about 34,884,587 (263)

Learning Health System Implementation: Building a Hub-and-Spoke Model for Hypertension Management Through the QI Hub. [PDF]

J Gen Intern Med
Alain G, Rush LJ, Summers R, Purdy N, Eramo J, Jonaus S, Cooper K, Nair S, Smarr C, Sneed A, VanBuren A, Teuschler L, Alain J, Quatman-Yates C.   +13 more
europepmc   +1 more source

Mitigating Creep Anisotropy and Enhancing Performance in Additively Manufactured Ni‐Based Superalloys: A Review

Advanced Engineering Materials, EarlyView.
Additive manufacturing introduces pronounced microstructural and crystallographic anisotropy in Ni‐based superalloys, resulting in orientation‐dependent creep deformation and damage. This review identifies the key microstructural origins of creep anisotropy, examines their influence on dominant deformation mechanisms, and highlights processing ...
Chandan Kumar, Shobhit Pratap Singh, Gaurav Mohanty, Praveen Kumar   +3 more
wiley   +1 more source

Establishing a learning agenda for learning health system implementation and research in Canada. [PDF]

PLoS One
Whitmore C, Bird M, Vanderhout S.
europepmc   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source

The learning health system imperative in low-resource contexts. [PDF]

Learn Health Syst
Lewicki P, Swarray-Deen A, Moyer CA.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study. [PDF]

NPJ Digit Med
Despraz J, Matusiak R, Nektarijevic S, Rossetti V, Bastardot F, Akrour R, Konasch A, Gauthiez E, Pignolet O, Pepe S, Chiche JD, Kaufmann DE, Calandra T, Raisaro JL, Meylan S, CHUV Sepsis consortium.   +15 more
europepmc   +1 more source

Intermediate Resistive State in Wafer‐Scale Vertical MoS2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

Advanced Functional Materials, EarlyView.
In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa, Milan Buttberg, Ke Ran, Rana Walied Ahmad, Dennis Braun, Lukas Völkel, Jimin Lee, Sofía Cruces, Bart Macco, Bárbara Canto, Holger Lerch, Thorsten Wahlbrink, Holger Kalisch, Michael Heuken, Andrei Vescan, Joachim Mayer, Zhenxing Wang, Ilia Valov, Stephan Menzel, Max C. Lemme   +19 more
wiley   +1 more source

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS). [PDF]

J Natl Med Assoc
Wi CI, Overgaard S, Malik M, Watson D, Sharma D, Le-Rademacher J, Kelleher D, Zheng L, Ohde J, Lynch B, Khurana A, Sohn S, AlJuhani MM, Carpenter C, Arteta M, Greenwood J, Hartz M, Foss R, Polat E, Absah I, Davis C, Gupta M, Myers L, Nordlund B, Tao C, Wieland ML, Patten C, Jones A, Varkey P, Loufek BT, Vidal DE, Gopala N, Klee E, Shah VH, Halamka JD, Callstrom MR, Rider NL, Liu H, Aliferis C, D Garovic V, J Juhn Y.   +40 more
europepmc   +1 more source